eprintid: 34524
rev_number: 10
eprint_status: archive
userid: 12259
dir: disk0/00/03/45/24
datestamp: 2019-04-11 08:18:10
lastmod: 2019-04-11 08:18:10
status_changed: 2019-04-11 08:18:10
type: thesis
metadata_visibility: show
creators_name: Tontowi Prasetyo, NIM. 13650050
title: PENGENALAN POLA RAMBU LALU LINTAS
MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT
ANALYSIS DAN HISTOGRAM OF ORIENTED GRADIENT
SEBAGAI EKSTRAKSI FITUR CITRA
ispublished: pub
subjects: TB
divisions: jur_tinf
full_text_status: restricted
keywords: Principal Component Analysis, Histogram of
Oriented Gradient, Pattern Recognition, Traffic Sign, Artificial
Neural Network.
note: Dr. Shofwatul Uyun, S.T., M.Kom
abstract: Research on the traffic sign recognition begin recently in line
with the growing popularity of intelligent vehicle systems. Traffic
sign recognition has its own challenges which include
pre-processing, feature extraction, also detection and
classification processes. In addition, the condition of signs when
images are taken such as lighting conditions and image clarity can
also affect the classification process. The purpose of conducting
this research is to support the system of smart driver and car
assistants.
This research aims to analyze the process of introducing
traffic signs using the Principal Component Analysis algorithm
and Histogram of Oriented Gradient as feature extraction. Other
parameters in the introduction process are also used, such as
pre-processing and classification using artificial neural networks.The testing scenario includes three pre-processing parameters
including: first parameter - Median Blur, Grayscale, Histogram
Equalization, Binary, and Canny; second parameter - Graysale,
Histogram Equalization, Binary, and Canny; and the third
parameter is using no pre-processing. Whereas, for neural
network structure using four hidden layer parameters: 10, 50, 100,
and 150.
The training process is carried out by combining
predetermined scenarios. The training results are grouped into two
major parts based on feature extraction. The Histogram of
Oriented Gradient Algorithm has a high accuracy compared to the
Principal Component Analysis algorithm with a maximum
accuracy of 100%. While the Principal Component Analysis
algorithm has a maximum accuracy of 79.38%. In the testing
process, the Histogram of Oriented Gradient algorithm has an
accuracy of 80.84%, while the Principal Component Analysis
algorithm only has the highest accuracy of 23.79%.
date: 2018-09-12
date_type: published
pages: 130
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Tontowi Prasetyo, NIM. 13650050  (2018) PENGENALAN POLA RAMBU LALU LINTAS MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS DAN HISTOGRAM OF ORIENTED GRADIENT SEBAGAI EKSTRAKSI FITUR CITRA.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/34524/1/13650050_Bab%20I_Bab%20Terakhir_Daftar%20Pustaka.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/34524/2/13650050_Bab%20II_SD_SEBELUM_BAB_TERAKHIR.pdf